比值法下基于不同选择策略的遗传算法换料优化比较分析

李湛; 周旭华; 丁铭; 黄杰

doi:10.13832/j.jnpe.2021.05.0023

比值法下基于不同选择策略的遗传算法换料优化比较分析

doi: 10.13832/j.jnpe.2021.05.0023

李湛^1,,
周旭华²,
丁铭^1, ,,
黄杰³

1.
哈尔滨工程大学黑龙江省核动力装置性能与设备重点实验室，哈尔滨，150001
2.
92609部队，北京，100077
3.
中广核研究院有限公司，广东深圳，518026

基金项目: 国家自然科学基金（11405036）

详细信息

作者简介:
李　湛（1997—），男，硕士研究生，现主要从事核反应堆换料优化算法的研究，E-mail: lz3225115@163.com

通讯作者:
丁　铭，E-mail: dingming@hrbeu.edu.cn

中图分类号: TL329
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- 被引次数: 0
出版历程
- 收稿日期: 2020-11-11
- 修回日期: 2021-06-21
- 刊出日期: 2021-09-30

Comparative Analysis of Genetic Algorithms Based on Different Selection Strategies for Refueling Optimization in the Ratio Method

Li Zhan^1
,,
Zhou Xuhua²,
Ding Ming^{1
, ,},
Huang Jie³

1.
Heilongjiang Provincial Key Laboratory of Nuclear Power System Performance & Equipment, Harbin Engineering University, Harbin, 150001, China
2.
92609 Unit, Beijing, 100077, China
3.
China Nuclear Power Technology Research Institute Co., Ltd., Shenzhen, Guangdong, 518026, China

摘要

摘要: 遗传算法是一种应用于反应堆换料优化问题的经典算法，该算法的一个重要组成部分为选择策略。在目前的文献中，选择策略常直接选轮盘赌选择法或随机竞争选择法，缺乏对不同选择策略的比较与分析。为得到寻优能力最强的选择策略，本研究以钍基柱状高温气冷堆1/6堆芯为例，以比值法构造适应度函数，利用DRAGON程序进行堆芯物理计算，结合精英保留策略，对轮盘赌选择法、随机竞争选择法、均匀排序法、指数排序法和确定式选择法5种选择策略的寻优能力进行了比较分析。分析结果表明，在这5种选择策略中，指数排序法的寻优能力最强，是最适合求解换料优化问题的选择策略。
- 钍基柱状高温气冷堆 /
- 换料优化 /
- 遗传算法 /
- 选择策略 /
- 比值法
Abstract: The genetic algorithm is one of the classic algorithms applied to the refueling optimization. An important part of this algorithm is the selection strategies. The existing studies often directly adopt the roulette wheel selection and stochastic tournament selection, and are lacking in comparison and analysis of different selection strategies. To obtain the selection strategy with the strongest optimization capability, this study, with the 1/6 core of a thorium-based prismatic high-temperature gas-cooled reactor (HTGR) taken as an example, constructs the fitness function in the ratio method, performs core physics calculation using the DRAGON code, and in conjunction with the elitism strategy, compares the optimization capabilities of the five selection strategies, including the roulette wheel selection, stochastic tournament selection, uniform ranking method, exponential ranking selection and deterministic selection. The study results show that the optimization capability of the exponential ranking selection is superior to the other four strategies, so the exponential ranking selection is most suitable for solving the refueling optimization problems.
- Thorium-based prismatic HTGR /
- Refueling optimization /
- Genetic algorithm /
- Selection strategies /
- Ratio method

HTML全文

图 1 钍基柱状高温气冷堆1/6堆芯示意图

Figure 1. Schematic Diagram of 1/6 Core of Thorium-Based Prismatic HTGR

下载: 全尺寸图片幻灯片

图 2 计算程序流程图

Figure 2. Flow Chart of Calculation Code

下载: 全尺寸图片幻灯片

图 3 不同c值指数排序选择适应度函数值收敛趋势

Figure 3. Convergence Trend of Fitness Function Values for Exponential Ranking Selection Corresponding to Different c Values

下载: 全尺寸图片幻灯片

图 4 不同遗传算法适应度函数值收敛趋势

Figure 4. Convergence Trend of Fitness Function Values of Different Algorithms

下载: 全尺寸图片幻灯片

图 5 轮盘赌选择法得到的最优燃料组件布置方式

Figure 5. Optimal Arrangement of Fuel Assemblies Obtained by Roulette Wheel Selection

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图 6 随机竞争选择法（随机遍历）得到的最优燃料组件布置方式

Figure 6. Optimal Arrangement of Fuel Assemblies Obtained by Stochastic Tournament Selection (Stochastic Ergodic)

下载: 全尺寸图片幻灯片

图 7 指数排序法（c=0.6）得到的最优燃料组件布置方式

Figure 7. Optimal Arrangement of Fuel Assemblies Obtained by Exponential Ranking Selection (c=0.6)

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表 1 不同c值指数排序适应度函数值

Table 1. Fitness Function Values for Exponential Ranking Selection Corresponding to Different c Values

c	适应度函数值	c	适应度函数值
0.1	0.726011	0.6	0.730443
0.2	0.721447	0.7	0.721562
0.3	0.721939	0.8	0.721453
0.4	0.720984	0.9	0.723934
0.5	0.722818

下载: 导出CSV

表 2 不同选择策略的适应度函数值

Table 2. Fitness Function Values for Different Selection Strategies　　　　　

选择策略	适应度函数值
轮盘赌选择法	0.723576
随机竞争选择法（随机遍历）	0.717843
随机竞争选择法（全遍历）	0.716831
确定式选择	0.716290
均匀排序	0.722588
指数排序（c=0.6）	0.730443

下载: 导出CSV

参考文献(16)

[1]	黄杰,李文强,丁铭. 遗传算法在柱状高温气冷堆换料优化问题中的应用[J]. 强激光与粒子束,2017, 29(1): 11-17.
[2]	夏冰,吕应中,经荥清,等. 熔盐球床堆堆芯燃料管理优化初步分析[J]. 原子能科学技术,2013, 47(增刊): 150-155.
[3]	刘仕倡,蔡杰进. 基于粒子群优化算法的压水堆换料优化初步研究[J]. 核动力工程,2013, 34(5): 1-5. doi: 10.3969/j.issn.0258-0926.2013.05.001
[4]	TAYEFI S, PAZIRANDEH A. Using hopfield neural network to optimize fuel rod loading patterns in VVER/1000 reactor by applying axial variation of enrichment distribution[J]. Applied Soft Computing Journal, 2014(21): 501-508.
[5]	周明, 孙树栋. 遗传算法原理及应用[M]. 北京: 国防工业出版社, 2000: 45-57.
[6]	咸春宇,左劼,于中华. 遗传算法在压水堆核电厂低泄漏换料堆芯装载方案优化中的应用[J]. 核动力工程,2002, 23(S1): 12-16.
[7]	JALUVKA D, EYNDE G V, VANDEWALLE S. Development of a core management tool for MYRRHA[J]. Energy Conversion and Management, 2013(74): 562-568.
[8]	ALIM F, IVANOV K N, YILMAZ S, et al. New genetic algorithms (GA) to optimize PWR reactors[J]. Annals of Nuclear Energy, 2007, 35(1): 113-120.
[9]	石秀安,胡永明. 我国钍燃料循环发展研究[J]. 核科学与工程,2011, 31(3): 281-288.
[10]	高立本,沈健. 高温气冷堆的发展与前景[J]. 中国核工业,2016(10): 24-26+55.
[11]	CHAPOT J L C, SILVA F C D, SCHIRRU R. A new approach to the use of genetic algorithms to solve the pressurized water reactor's fuel management optimization problem[J]. Annals of Nuclear Energy, 1999, 26(7): 641-655. doi: 10.1016/S0306-4549(98)00078-4
[12]	KUMAR A, TSVETKOV P V. A new approach to nuclear reactor design optimization using genetic algorithms and regression analysis[J]. Annals of Nuclear Energy, 2015(85): 27-35.
[13]	DESCOTES V M. Reactor physics of a deep-burner prismatic core for VHTR[D]. Montréal: Université de Montréal, 2011.
[14]	NOROUZI A, ZOLFAGHARI A, MINUCHEHR A H, et al. An enhanced integer coded genetic algorithm to optimize PWRs[J]. Progress in Nuclear Energy, 2011, 53(5): 449-456. doi: 10.1016/j.pnucene.2011.03.005
[15]	CHAKRABORTY M, CHAKRABORTY U K. An analysis of linear ranking and binary tournament selection in genetic algorithms[C]. Proceedings of ICICS, 1997 International Conference on Information, Communications and Signal Processing. Theme: Trends in Information Systems Engineering and Wireless Multimedia Communications, 1997, 1: 407-411.
[16]	TSAI M C, CAI Y X, WANG C D, et al. Tinnitus abnormal brain region detection based on dynamic causal modeling and exponential ranking[J]. BioMed Research International, 2018(2018): 1-10.